Search Results (23,553)

(1) Background: Rapid global aging has surged demand for elderly family care, a role long dominated by women. This study aims to reveal the specific manifestations of the gender division of labor in elderly family care through a systematic evidence synthesis, covering care tasks, care types, impacts, and driving factors. (2) Methods: We searched four databases (Web of Science (SSCI subsets), Scopus, PubMed, and ProQuest) for articles published between 2015 and 2025. After screening, 45 peer-reviewed articles from 16 countries or regions were included, and thematic analysis was employed for data extraction and evidence synthesis. (3) Findings: The results indicate a differentiated gender division of labor and inequality in elderly family care, where female caregivers bear a greater burden in terms of task assumption, care time allocation, and perception of care impacts. The formation of the gender division of labor results from a dynamic interplay among multiple factors, including objective needs, social norms, and institutional influences. Promisingly, men are increasingly participating in family care for the elderly. (4) Conclusions: The study suggests that gender-sensitive policies should address the gender gap for elderly family care and provide targeted support to alleviate the unequal distribution of care burdens. Full article

Background/Objectives: Aortic stenosis is associated with autonomic nervous system (ANS) imbalance, while diabetes mellitus is a major contributor to cardiac autonomic neuropathy. Their coexistence may result in more pronounced autonomic dysfunction not fully captured by conventional assessment. This study aimed to compare ANS function in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement (TAVR), according to diabetes status. Methods: This cross-sectional study included 74 patients with severe aortic stenosis referred for TAVR, including 21 patients with diabetes mellitus. Autonomic function was evaluated using non-invasive ECG-based analysis, incorporating short-term and 24 h Holter-derived heart rate variability (HRV), nonlinear Poincaré plot indices, and deceleration and acceleration capacity. Ambulatory blood pressure monitoring and standard clinical and echocardiographic assessment were performed. Results: Patients with diabetes mellitus demonstrated significantly lower long-term HRV parameters and reduced nonlinear Poincaré plot indices compared with non-diabetic patients, indicating altered autonomic modulation. Short-term HRV showed similar trends without statistical significance. Echocardiographic severity of aortic stenosis and left ventricular systolic function were comparable between groups. Conclusions: Autonomic dysfunction appears to be more pronounced in patients with severe aortic stenosis and diabetes mellitus, predominantly affecting parasympathetic modulation. ECG-derived autonomic parameters may offer complementary insight into ANS involvement in this population and warrant further investigation. Full article

The decarbonisation of the transport sector is a cornerstone of the European Green Deal, necessitating a transition toward integrated, digital, and sustainable mobility solutions such as Mobility-as-a-Service (MaaS). While early MaaS implementations were characterised by local bottom-up experiments, recent state-level actions mark a shift toward large-scale systemic deployment. This paper investigates the evolving role of universities within this transition, using MaaS4Italy initiative as a primary case study. Through a qualitative analysis of 11 pilot projects, conducted between January and July 2025, the research examines how academic institutions have been integrated into the national governance framework, transitioning from traditional living labs for technical testing to pivotal institutional anchors and governance buffers. The findings reveal a dual role for universities: as scientific partners and as neutral mediators. However, a relevant paradox is highlighted as well: while the institutionalisation of universities de-risks public investment and fosters data-sharing trust, it may simultaneously limit their potential as high-density operational testbeds for innovative Corporate MaaS (CMaaS) solutions. Present research supports a broader understanding for policymakers, thus underscoring the importance of formalising the role of intermediary institutions to ensure the long-term sustainability and scalability of smart mobility ecosystems. These insights prove to be pivotal towards the implementation of multi-level environmental governance mechanisms and the strategic use of recovery funds to catalyse the transition toward climate neutrality. Full article

The incorporation of supplementary cementitious materials (SCMs) is a key strategy for enhancing the performance and sustainability of cement-based systems. This research examines the mechanical behavior, microstructural evolution, and durability-related properties of cementitious materials incorporating waste glass powder (WGP) and metakaolin (MK) as partial replacements of Portland cement. Cement pastes were evaluated for compressive strength at 7 and 28 days, while microstructural analysis at 28 days employed gas adsorption and scanning electron microscopy (SEM). Based on the compressive strength performance of the cement pastes, ternary WGP–MK mortars were assessed for consistency, flexural and compressive strength, water absorption, and porosity at 28 and 60 days. Results indicate that MK accelerates early-age strength, whereas WGP enhances long-term performance and pore structure refinement. Binary and ternary systems exhibited reduced accessible pore volume, enhanced microstructural homogeneity, and lower water absorption with curing time. The findings demonstrate that WGP-MK blends support clinker reduction without compromising performance, advancing circular economy goals in construction. Full article

The Copernicus Sentinel-2 (S2) data are a crucial resource for urban policymakers in land-cover classification, offering a freely accessible alternative to expensive commercial data sources. While medium spatial resolution often limits the applicability of data-intensive machine learning approaches, the Geographic Object-Based Image Analysis (GEOBIA) framework could be an effective, operational alternative for urban land-cover classification using S2 data. This study applies the Geographic Object-Based Image Analysis (GEOBIA) approach to classify land cover in Hue, Vietnam, using Sentinel-2 data processed through the eCognition interface. The study’s findings emphasize the potential of GEOBIA and S2 data in enhancing decision-making processes for city authorities, ensuring better resource allocation, environmental protection, and infrastructure development. The results indicate that the method performs reliably for mesoscale and spatially continuous classes, such as vegetation and built-up surfaces, while accuracy is lower for small or spectrally heterogeneous features, particularly shallow water bodies and fragmented rice paddies, due to mixed-pixel effects inherent in 10–20 m resolution imagery. The results demonstrate an Overall Accuracy (OA) of 91%, highlighting the method’s effectiveness in extracting and classifying urban land-cover classes. This study demonstrates a replicable model for urban land monitoring that can be adapted across various geographic contexts. Furthermore, this approach fosters a more data-driven governance model, where urban expansion and land-use changes can be monitored in real time, allowing for proactive interventions. With urbanization accelerating worldwide, particularly in rapidly developing regions, such a cost-effective and accessible classification method can significantly aid in achieving long-term urban sustainability. The findings illustrate the relevance of GEOBIA as a feasible tool for supporting data-driven urban governance, enabling systematic tracking of land-use change, informed infrastructure planning, and sustainable urban management in both developed and rapidly urbanizing regions. Full article

Background: Multiple sclerosis (MS) is a long-term and unpredictable condition that can cause considerable psychological distress, including perceived stress and death anxiety. Identifying psychological factors that may mitigate these effects is important for improving the psychosocial well-being of patients with MS. This study aimed to examine the mediating role of self-compassion in the relationship between perceived stress and death anxiety in patients with MS. Methods: This cross-sectional, descriptive, and correlational study included 169 Turkish patients diagnosed with MS between October 2024 and April 2025. A regression-based mediation analysis using the Hayes PROCESS macro with bootstrapping was conducted to assess the mediating role of self-compassion. Results: Death anxiety scores were positively but weakly correlated with perceived stress scores (r = 0.172, p = 0.026). Perceived stress scores were strongly and negatively correlated with self-compassion scores (r = −0.704, p < 0.001), whereas self-compassion scores showed a weak-to-moderate negative correlation with death anxiety scores (r = −0.287, p < 0.01). In the mediation model, perceived stress significantly predicted self-compassion (B = −0.087, p < 0.001), and self-compassion significantly predicted death anxiety (B = −1.758, p < 0.001). The direct effect of perceived stress on death anxiety was not statistically significant (B = −0.058; p = 0.344), whereas the indirect effect was significant (B = 0.153; 95% CI [0.079, 0.232]). The total effect was also significant (B = 0.095; p = 0.036). Conclusions: The findings indicate that self-compassion mediates the relationship between perceived stress and death anxiety in patients with MS. Higher levels of self-compassion were associated with lower levels of perceived stress and death anxiety, suggesting that self-compassion may function as an important psychological resource in coping with disease-related stress and death-related concerns. From a clinical and nursing perspective, integrating strategies that support self-compassion within holistic care may contribute to improving the psychosocial well-being of patients with MS. Full article

Foaming technology can effectively reduce the viscosity of polymer-modified asphalt and significantly decrease energy consumption during pavement construction, making it an effective approach for achieving low-carbon pavement construction and maintenance. However, mechanically foamed asphalt relies on specialized equipment and requires strict parameter control. Although water-based foaming methods using zeolites or ethanol can alleviate these issues to some extent, they still present disadvantages such as significant variability in foaming performance and potential risks during transportation and construction. Therefore, this study investigates the feasibility of using crystalline hydrates with high water of crystallization for micro-foamed asphalt. Three types of micro-foamed SBS-modified asphalt (MFPA) were prepared using hydrates with different contents of water of crystallization. Physical property tests, foaming characteristic parameters, viscosity–temperature analysis, Fourier transform infrared spectroscopy (FTIR), adhesion tensile tests, scanning electron microscopy (SEM), and fluorescence microscopy were conducted to evaluate their effects on the physical and chemical properties, viscosity reduction performance, adhesion, and compatibility of SBS-modified asphalt. Furthermore, dynamic shear rheometer (DSR) tests, bending beam rheometer (BBR) tests, fatigue life modeling, and morphological analysis were employed to investigate the rheological properties, fatigue life, and bubble evolution behavior of the MFPA system. The results indicate that utilizing the thermal decomposition characteristics of crystalline hydrates with high water of crystallization (Na₂SO₄·10H₂O, Na₂HPO₄·12H₂O, and Na₂CO₃·10H₂O) to release H₂O and CO₂ in SBS-modified asphalt for micro-foaming is a short-term reversible physical viscosity reduction process. The maximum expansion ratio (ERmax) of MFPA reaches 8–10, the half-life (HL) remains stable at approximately 180 s, and the foaming index (FI) peak is about 1160. The construction temperature can be reduced by 10–15%, and the viscosity reduction effect remains stable within 60 min. Compared with unfoamed SBS-modified asphalt, the compatibility, rutting resistance, and fatigue life of MFPA increase by approximately 65%, 32%, and 30%, respectively, while the low-temperature performance decreases by 18%. Under the same short-term and long-term aging conditions, MFPA exhibits better aging resistance. Specifically, its rutting resistance increases by 37%, and fatigue resistance improves by 30% compared with aged SBS-modified asphalt, while the low-temperature performance remains essentially unchanged. Full article

Optimizing drug administration remains a central challenge in the development of modern therapies, especially in the context of conditions that require spatiotemporal control of active substance release. In this context, hydrogels have been intensively investigated as polymeric platforms for drug delivery, through their three-dimensional hydrophilic structure, tunable properties, and compatibility with biological environments. This analysis presents an integrated approach to hydrogels used in drug administration, addressing the physicochemical fundamentals, the constitutive polymeric materials, and the mechanisms of response to relevant physiological stimuli. Recent experimental studies have been discussed, which highlight the use of hydrogels based on natural, synthetic, and hybrid polymers for controlled and targeted release, in correlation with various administration routes, including oral, injectable, transmucosal, and topical ones. Advanced functionalization strategies that allow adaptive responses to pH, temperature, glucose, enzymes, and reactive oxygen species are also analyzed. Furthermore, emerging directions integrating hydrogels with biosensors, microdevices, and wireless communication systems for real-time monitoring and on-demand release are highlighted. Overall, the analysis emphasizes the role of smart hydrogels as multifunctional platforms for complex therapeutic strategies while also underlining the current challenges associated with clinical translation and long-term performance. Full article

Horizontal transfer of mitochondrial DNA into the nuclear genome generates nuclear mitochondrial sequences (NUMTs), which serve as molecular fossils reflecting long-term mitochondrial–nuclear interactions and genome evolution. However, the biological mechanisms governing NUMT integration, retention, and evolutionary fate remain incompletely understood in domesticated animals. Here, using the latest pig reference genome assembly (Sscrofa11.1), we present a comprehensive genome-wide characterization of NUMTs in pigs and provide new insights into their genomic distribution and evolutionary constraints. We identified 513 high-confidence NUMTs, of which 460 were chromosomally mapped, accounting for 0.0106% of the nuclear genome. Beyond increased detection, our analyses reveal that pig NUMTs exhibit non-random origins, preferentially integrate into genomic regions under weak selective constraint, and are frequently associated with repetitive elements, consistent with a DNA repair-mediated insertion mechanism. NUMTs predominantly occur as short, fragmented sequences and show signatures of long-term neutral evolution, while insertions disrupting coding sequences are strongly selected against. Synteny-based analyses further identified clustered NUMT regions and duplicated NUMTs, suggesting secondary genomic duplication events following initial integration. Comparative analysis with the earlier Sscrofa10.2 assembly demonstrates that improved genome quality substantially enhances NUMT detection, particularly in repetitive and GC-rich regions, clarifying previously ambiguous sequence-context associations. Together, this high-quality pig NUMT map provides a robust foundation for future functional, evolutionary, and population-level investigations and contributes to the conservation and utilization of pig genetic resources. Full article

Soil pore architecture and hydraulic functioning strongly regulate water flow and retention. However, despite the growing application of X-ray computed tomography (X-ray CT) in soil science, its application in characterizing the pore system and hydraulic functioning of native forest soils converted to sugarcane production systems in northeast Brazil is still poorly known. This study therefore quantified the soil structure, pore system, and hydraulic functioning of a native forest (NF) and an adjacent sugarcane field receiving vinasse and managed without intercropping (sole sugarcane (SG)) and with Brachiaria ruziziensis intercropping (SG + Bra intercrop) in northeastern Brazil, using conventional soil physical measurements and X-ray CT, in three soil layers (0–10, 10–20, and 20–40 cm). Soil physical and hydraulic properties, as well as soil water retention, were quantified. The native forest soil exhibited a uniformly sandy texture across all depths, whereas sugarcane systems ranged from loam to sandy textures in surface layers due to long-term management. Soil organic matter and total nitrogen in the 0–10 cm layer were approximately 75 and 65% higher, respectively, in sole Sole SG and SG + Bra intercrop than in NF. Soil bulk density increased with depth under sugarcane, reaching values about 10%–13% higher than NF in the 20–40 cm layer. Saturated hydraulic conductivity in the surface layer was higher in the NF, approximately five to nine times greater than in sole SG and SG + Bra intercrop, respectively. Conventional water retention analysis showed that sole SG and SG + Bra intercrop had greater total porosity (0.49–0.55 m³ m⁻³), microporosity (0.26–0.36 m³ m⁻³), field capacity (0.19–0.33 m³ m⁻³), and plant available water (0.09–0.15 m³ m⁻³) in the upper 20 cm compared with the NF (≤0.10 m³ m⁻³ available water). In contrast, X-ray CT revealed higher macroporosity (0.20–0.23 mm³ mm⁻³) and pore connectivity in the NF across all depths, with predominantly complex, inclined to near-horizontal pores and low anisotropy. Intercropping sugarcane with Brachiaria did not significantly alter (p > 0.05) bulk density, hydraulic conductivity, or CT-derived pore connectivity relative to sole sugarcane. The degree of anisotropy and fractal dimension derived from X-ray CT were significantly correlated (p < 0.05) with conventionally measured hydraulic properties. The X-ray computed tomography proved effective in linking pore-scale architecture to soil hydraulic functioning, providing insights beyond conventional measurements. The short-term inclusion of Brachiaria as a cover crop at 10 kg seed ha⁻¹ did not result in significant improvements in soil pore structure, indicating that longer-term adoption and/or higher planting densities may be required to induce measurable changes in pore system architecture and soil hydraulic functioning. Full article

Environmental pressure in Nigeria persistently escalates despite several development efforts, prompting questions about the structural factors contributing to the nation’s ecological vulnerability. Considering this, the study employs a time-series research design that synthesizes collective theoretical perspectives to elucidate the interplay between income inequality (GINI), institutional quality (INST), trade in services (TO), and population density (POPd) in shaping Nigeria’s ecological footprint (ECF), utilizing data for the aforementioned variables from 1960 to 2024. The analysis shows time-varying dynamics across pollution regimes using Quantile-on-Quantile Regression (QQR) and Wavelet Quantile Correlation (WQC). The result reveals notable asymmetries across the ECF distribution. GINI and POPd intensify ecological pressure mainly at higher ECF quantiles. While INST serves as a key mitigating factor of ECF, particularly in a long-term pollution scenario. TO exhibits a regime-dependent effect, aligning with the Pollution Haven expectation in poor environments. These findings suggest that environmental outcomes in emerging economies are shaped by structural inequality and institutional strength. Highlighting the necessity of building institutional capacity to decouple inequality that drives ecological degradation. Thus, connecting national strategies with the Sustainable Development Goals (SDGs) 1, 8, 10, 11, 12, 13, 15, 16, and 17. These provide actionable insights into an inclusive and resilient environment. Full article

Background and Objectives: Male infertility has emerged as a growing global health concern, contributing to 20–30% of all infertility cases. It is a multifactorial condition, arising from genetic, endocrine, structural, environmental and lifestyle factors. This narrative review synthesizes current evidence on epidemiology, diagnostic advances and therapeutic strategies while highlighting emerging trends and research priorities. Materials and Methods: This review adheres to SANRA guidelines. Literature was sourced from PubMed, Saudi Digital Library, Google Scholar, and PsycINFO using MeSH terms including “Male Infertility,” “Diagnosis,” “Treatment,” and “Epidemiology.” Results: Diagnostic evaluation of male infertility includes clinical assessment, advanced semen analysis, imaging techniques, hormonal assays and molecular testing. Despite significant advances in the evaluation of male infertility, idiopathic causes (30–40%) remain challenging. Management strategies include lifestyle modifications, medical therapies including hormones and drugs, surgical interventions, and assisted reproductive technologies (ARTs). However, outcomes remain suboptimal in idiopathic and severe cases, particularly regarding sperm DNA fragmentation and environmental exposures. Conclusions: Substantial knowledge gaps exist in male infertility, particularly in idiopathic cases, molecular mechanisms of environmental pollutants, and long-term ART offspring outcomes. Future research priorities include: (1) molecular and epigenetic biomarkers for improved diagnosis and prognosis; (2) environmental exposure assessment and mitigation strategies; (3) metabolomics-guided personalized therapies; (4) regenerative medicine approaches including spermatogonial stem cell therapy; and (5) multidisciplinary integrative care models. Addressing these gaps through coordinated research and clinical innovation is essential for improving male reproductive health globally. Full article

Objective: The aim of this study was to evaluate serum homocysteine levels in obese children and adolescents and to examine their relationships with insulin resistance, metabolic risk factors, and vitamin B12, folate, and vitamin D status. Methods: A single-center, retrospective cross-sectional observational study included 102 children and adolescents with obesity attending a tertiary pediatric obesity clinic. Clinical, anthropometric, and biochemical data were analyzed. Group comparisons were performed according to obesity severity and presence of hyperhomocysteinemia. Correlation analyses and multivariable linear regression were conducted to identify independent predictors of serum homocysteine levels. Results: The median serum homocysteine level was 9.5 (7.82–11.8) µmol/L, and hyperhomocysteinemia was present in 27.5% of cases. Insulin resistance was significantly more prevalent in children with severe obesity compared to those with obesity (90.6% vs. 64.3%; OR 5.29, 95% CI 1.41–29.8; p = 0.008). Serum homocysteine levels were positively correlated with age, BMI, fasting glucose, insulin, and HOMA-IR, and negatively correlated with vitamin B12 and folate levels (all p < 0.05). Folate deficiency was significantly more common in participants with hyperhomocysteinemia (33.3% vs. 6.7%; OR 6.82, 95% CI 1.80–29.37; p = 0.002). In multivariable regression analysis, age (β = 0.433; p = 0.001) and folate levels (β = −0.235; p = 0.032) were independently associated with serum homocysteine concentrations. Conclusions: Hyperhomocysteinemia is present in approximately one-quarter of children with obesity and may represent a relevant metabolic alteration in this population. Although serum homocysteine levels were correlated with insulin resistance in univariable analyses, multivariable regression analysis identified age and folate levels as independent determinants. These findings highlight the potential clinical importance of evaluating folate status in children with obesity, particularly in those with elevated homocysteine levels. Prospective studies are warranted to determine whether folate supplementation can effectively reduce homocysteine levels and improve long-term cardiometabolic risk in pediatric obesity. Full article

Forecasting cryptocurrency prices is challenging due to extreme volatility, nonlinear dynamics, and frequent structural shifts in digital asset markets. While recent research increasingly applies deep learning architectures, the predictive advantage of highly complex models in noisy financial environments remains uncertain. This study evaluates the forecasting performance of shallow and deep learning approaches by comparing Support Vector Machines (SVM), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models, along with hybrid configurations (GRU + SVM, LSTM + SVM, and GRU + LSTM). Using daily data spanning from 1 October 2020 to 23 September 2025 for five major cryptocurrencies—Bitcoin, Ethereum, Binance Coin, Solana, and Ripple—the models are estimated within a consistent framework and assessed using out-of-sample performance metrics, including MAE, MAPE, MSE, and R². The results indicate that greater algorithmic complexity does not necessarily improve forecasting accuracy. In several cases, the parsimonious SVM model outperforms deep neural network architectures, particularly for highly volatile assets, while hybrid models fail to provide systematic improvements and sometimes amplify prediction errors. SHapley Additive exPlanations analysis further shows that immediate price-based variables dominate predictive power, whereas many lagged technical indicators contribute relatively limited explanatory value. Overall, the findings underscore the importance of algorithmic parsimony, suggesting that simpler machine learning models may deliver more robust forecasts in highly volatile cryptocurrency markets. Full article

Advanced high-temperature fluid reactors (ARs), such as sodium fast reactors (SFRs) and molten salt cooled reactors (MSCRs) utilize high-temperature fluids at ambient pressure. To melt the fluid during reactor startup and prevent fluid freezing during cooldown, the thermal–hydraulic systems of such ARs include heater zones consisting of specific heaters with controllers, temperature sensors, and thermal insulation. The failure of heater zones due to insulation material degradation or improper installation, resulting in parasitic heat losses, can lead to fluid freezing. The detection of faults using a heat-transfer model is difficult because of a lack of knowledge of the experimental details. Data-driven machine learning of heater zone temperature time series offers a viable alternative. In this study, we benchmarked the performance of recurrent neural networks (RNNs) in an analysis of heat-up transient temperature time series of heater zones installed on a liquid sodium vessel. The RNN models include long short-term memory (LSTM) and gated recurrent unit (GRU) networks, as well as their bi-directional variants, BiLSTM and BiGRU. Anomalous temperature points were designated using a percentile-based threshold applied to residual fluctuations in the detrended temperature time series. Additionally, the impact of the exponentially weighted moving average (EWMA) method on detection accuracy was examined. The RNN models’ performance was assessed using precision, recall, and F₁ score metrics. Results demonstrated that RNN models effectively detect anomalies in temperature time series with the best models for each heater zone achieving F₁ scores of over 93%. To explain the variations in RNN model performance across different heater zones, we used Kullback–Leibler (KL) divergence to quantify the relative entropy between training and testing data, and the Detrended Fluctuation Analysis (DFA) to assess long-range temporal correlations. For datasets with strong long-range correlations and minimal relative entropy between training and testing data, GRU is the best-performing model. When the data exhibits weaker long-term correlations and a significant relative entropy between training and testing distributions, BiGRU shows the best performance. For the data sets with intermediate values of both KL divergence and DFA, the best performance is obtained with LSTM and BiLSTM, respectively. Full article